Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 026

Affordable Uplift: Supervised Randomization in Controlled Exprtiments

Johannes Haupt
Daniel Jacob
Robin M. Gubela
Stefan Lessmann

Customer scoring models are the core of scalable direct marketing. Uplift models
provide an estimate of the incremental benefit from a treatment that is used for
operational decision-making. Training and monitoring of uplift models require
experimental data. However, the collection of data under randomized treatment
assignment is costly, since random targeting deviates from an established
targeting policy. To increase the cost-efficiency of experimentation and
facilitate frequent data collection and model training, we introduce supervised
randomization. It is a novel approach that integrates existing scoring models
into randomized trials to target relevant customers, while ensuring consistent
estimates of treatment effects through correction for active sample selection.
An empirical Monte Carlo study shows that data collection under supervised
randomization is cost-efficient, while downstream uplift models perform

Uplift Modeling, Causal Inference, Experimental Design, Selection Bias

JEL Classification: